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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Kadar, A.A. Parameshwaran, R. Rithanathith, S. Rao, Gurugubelli Vasudeva Lakshminarayanan, G. Department of ECE National Institute of Technology Tiruchirappalli India Department of ECE KPR Institute of Engineering and Technology Coimbatore India

ISBN: (纸本)9781607685395

adaptive sampling is a signal processing technique used in various aerospace applications. Many adaptive algorithms used for instrumentation and telemetry systems process the signal in the frequency domain, which leads to high computational cost and power. ASA-m solves this problem by performing all operations in the time domain. It estimated the subsequent sampling frequency to collect meaningful information based on mean velocity prediction. This novel algorithm is implemented on the Spartan 3E FPGA board to study the device power and hardware utilization for real-time vibration signal datasets. The significant recovery of data with a lesser number of samples and lower hardware utilization for the state of art algorithm ASA-m is brought out in this paper. © The Electrochemical Society

关键词： adaptive sampling algorithm Mean velocity prediction Vehicle health management Wireless Sensor network

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Application of adaptive sampling algorithm in solving the scattering field of UHVAC/DC transmission lines

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JOURNAL OF ENGINEERING-JOE 2019年第16期2019卷 1404-1407页

作者： Shi, Jia Tang, Bo Hao, Bin Yang, Jiawei China Three Gorges Univ Coll Elect Engn & New Energy Yichang 443002 Hubei Peoples R China

Using MBPE technology can quickly predict the approximation of the frequency response of transmission lines' scattering field. However, equal interval sampling method is usually used in the calculation of transmission lines' scattering field, it results a possibility that the sampled data may be with lack of important points, which makes the fitting results difficult to control, or samples duplicated information to increase the amount of calculation. In order to calculate more accurately, the adaptive sampling algorithm is introduced here. In the fast solution of frequency response of transmission lines' scattering field, it greatly improved the accuracy of results. Finally, compared with those of the traditional interval sampling methods, the results of using adaptive sampling algorithm were in line with the actual situation.

关键词： curve fitting parameter estimation frequency response sampling methods adaptive sampling algorithm scattering field UHVAC DC transmission lines frequency response equal interval sampling method sampled data traditional interval sampling methods

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Application and comparison of several adaptive sampling algorithms in reduced order modeling

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HELIYON 2024年第15期10卷 e34928页

作者： Liu, Xirui Wang, Zhiyong Ji, Hongjun Gong, Helin Univ Elect Sci & Technol China Sch Math Sci Chengdu 611731 Sichuan Peoples R China Shanghai Jiao Tong Univ Paris Elite Inst Technol Shanghai 200240 Peoples R China Nucl Power Inst China Chengdu 610041 Peoples R China

Model Order Reduction (MOR) techniques have extensive applications across scientific and engineering disciplines, such as neutron field reconstruction of nuclear reactor cores, thermoelastic field reconstruction, fluid, and solid mechanics. In the process of building a Reduced Order Model (ROM), the selection of the basis functions in the offline stage is crucial and directly depends on the parameter space sampling strategy. This problem has always been a challenge in MOR. Research into adaptive sampling algorithms has become a hot topic in recent years. To better understand the application of these algorithms to MOR, this paper focuses on three prevalent adaptive sampling algorithms: pseudo-gradient sampling, adaptive sparse grid sampling, adaptive training set extension. These have been successfully applied in various applications, including nuclear reactor cores, molten salt reactor system, power system for convection problems. We systematically assess and compare their performance, finding that adaptive sampling algorithms excel in sampling divergent and oscillating areas and are generally better than the standard sampling strategy. Specifically, the pseudo-gradient sampling algorithm is effective for small-scale scenarios, while the other two algorithms are designed for large-scale sampling. Their practicality is confirmed through successful applications in nuclear reactor cores.

关键词： adaptive sampling algorithm Reduced order model Pseudo-gradient sampling adaptive sparse grid sampling adaptive training set extension

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sampling-based test scenarios searching method for autonomous system robustness evaluation

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APPLIED SOFT COMPUTING 2024年 162卷

作者： Lu, Hui Wang, Shiqi Zhang, Yuxuan Cheng, Shi Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710062 Peoples R China

The robustness evaluation for autonomous systems is essential before implementing them in safety-critical fields. Searching the challenging test scenarios by adaptive sampling algorithm in the transition regions between different performance modes, named performance boundary, becomes an efficient method to evaluate the robustness of systems. This paper proposes a novel model-independent nearest neighbors adaptive sampling algorithm considering gradient and distance, named GDNNAS. GDNNAS designs a special evaluation criterion to identify samples within boundary regions and a searching method to generate new samples. A novel elimination criterion is proposed to ensure the quality of samples and an adaptive stopping criterion is proposed to stop the sampling process adaptively. Considering the lack of unified data sets and metrics to assess adaptive sampling methods for searching the performance boundary, we proposed a comparison platform consisting of nine simulated systems under test (SSUT) as adaptive sampling benchmarks and four evaluation metrics to evaluate the adaptive sampling method quantitatively. We assess GDNNAS and two other adaptive sampling algorithms with the nine SSUTs. The experimental result proves that samples generated by GDNNAS are precisely located at and cover the performance boundary regions. In addition, we analyze the effectiveness of the comparison platform. Experimental result shows that the proposed SSUTs and evaluation metrics can comprehensively reflect the performance of the algorithm from various aspects and reveal the characteristics of the adaptive sampling algorithms. To verify the practicability of GDNNAS, a path planning system is used for testing GDNNAS, and the experiment result proves that GDNNAS performs outstandingly in practical system robustness evaluations.

关键词： adaptive sampling algorithm Performance boundary Simulated system under test Evaluation metrics

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Most Efficient Sensor Network Protocol for a Permanent Natural Disaster Monitoring System

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IEEE INTERNET OF THINGS JOURNAL 2021年第15期8卷 11776-11792页

作者： Lee, Changmin Kim, Seong-Lyun Yonsei Univ Dept Elect & Elect Engn Seoul 03722 South Korea

To minimize enormous havoc from disasters, permanent environment monitoring is necessarily required. Thus, we propose a novel energy management protocol for energy harvesting wireless sensor networks, named the adaptive sensor node management protocol (ASMP). The proposed protocol makes system components to systematically control their performance to conserve the energy. Through this protocol, sensor nodes autonomously activate an additional energy conservation algorithm. ASMP embeds three sampling algorithms. For the optimized environment sampling, we proposed the adaptive sampling algorithm for monitoring (ASA-m). ASA-m estimates the expected time period to occur meaningful change. The meaningful change refers to the distance between two target data for the monitoring Quality of Service. Therefore, ASA-m merely gathers the data the system demands. The continuous adaptive sampling algorithm (CASA) solves the problem to be continuously decreasing energy despite of ASA-m. When the monitored environment shows a linear trend property, the sensor node in CASA rests a sampling process, and the server generates predicted data at the estimated time slot. For guaranteeing the self-sustainability, ASMP uses the recoverable adaptive sampling algorithm (RASA). RASA makes consumed energy smaller than harvested energy by utilizing the predicted data. RASA recharges the energy of the sensor node. Through this method, ASMP achieves both energy conservation and service quality.

关键词： Monitoring Quality of service Protocols Prediction algorithms Energy conservation Energy harvesting Energy consumption adaptive sampling algorithm disaster monitoring system energy conservation protocol energy harvesting wireless sensor networks (EH-WSNs) sampling period prediction self-sustainability

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Orthogonal single-pixel imaging using an adaptive under-Nyquist sampling method

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OPTICS COMMUNICATIONS 2021年 500卷

作者： Xu, Canwen Zhai, Aiping Zhao, Wenjing He, Peng Wang, Dong Taiyuan Univ Technol Coll Phys & Optoelect 79 West Main St Yingze 030024 Peoples R China Taiyuan Univ Technol Key Lab Adv Transducers & Intelligent Control Sys Minist Educ & Shanxi Prov 79 West Main St Yingze 030024 Peoples R China

Single-pixel imaging (SPI) recently has made significant progress, however, it is still an open issue to adaptively tradeoff its imaging fidelity and speed. Here, we present an adaptive sampling method for SPI to reduce the sampling rate for improving the imaging speed adaptively, sacrificing as little as possible of its imaging fidelity. The proposed method only requires setting an error band to realize adaptive under-Nyquist sampling, based upon the fact that the coefficient matrix consisted of the frequency components obtained by the orthogonal transforms is sparse. First, an appropriately elaborated sampling path, which can be divided into multiple segments, can be set according to this coefficient matrix measured by a single-pixel detector. Then the variance of each segment is calculated and expressed in logarithmic form for the real-time polynomial curve fitting. When the variation of the curve's slope is small enough and reaches an error band, the sampling is adaptively stopped. Finally, the object image can be reconstructed via the adaptive sampling method following the elaborated sampling path for improving the imaging speed adaptively. The effectiveness of the adaptive sampling SPIs based on the orthogonal transforms, such as Hadamard transform, discrete cosine transform, Fourier transform, and Krawtchouk moments transform, is theoretically and experimentally demonstrated. It should be noted the proposed method can be also applied in SPIs using other orthogonal transforms, in which only an error band is set by the users.

关键词： Single-pixel imaging Computational ghost imaging adaptive sampling algorithm Orthogonal transforms

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An adaptive sampling surrogate model building framework for the optimization of reaction systems

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COMPUTERS & CHEMICAL ENGINEERING 2021年 152卷

作者： Franzoi, Robert E. Kelly, Jeffrey D. Menezes, Brenno C. Swartz, Christopher L. E. Univ Sao Paulo Dept Chem Engn Av Prof Lineu Prestes 580 BR-05508000 Sao Paulo Brazil Ind Algorithms Ltd 15 St Andrews Rd Toronto ON M1P 4C3 Canada Hamad Bin Khalifa Univ Qatar Fdn Coll Sci & Engn Div Engn Management & Decis Sci Doha Qatar McMaster Univ Dept Chem Engn 1280 Main St W Hamilton ON L8S 4L7 Canada

Many industrial engineering problems involve complex formulations and are assisted by simulation tools. Although these tools provide highly accurate solutions, they may not be suitable for large scale problems and for optimization applications. Looking for alternatives to complex formulations that often lead to convergence issues and to time consuming solutions, the use of surrogate modeling for reaction systems is addressed herein. We propose a novel adaptive sampling algorithm that iteratively explores the solution space and incorporates ideas from adaptive sampling, trust region methods, and successive linear programming approaches. The surrogates are iteratively embedded into optimization problems to check feasibility and to collect insights to the following adaptive sampling iteration. The methodology is applied to a reaction system network and the surrogates are built to predict the reactor outputs. The adaptive sampling algorithm builds highly accurate surrogates that can be embedded into the reaction system optimization leading to near optimal solutions. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Surrogate modeling adaptive sampling algorithm Data-driven Machine learning Reactor systems

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SMART SHOES WITH adaptive sampling FOR OUTPATIENT DAILY HEALTH MONITORING

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Design of Medical Devices Conference

作者： Vuong, Julie Qiao, Zhi Zhang, Wenlong Arizona State Univ Ira A Fulton Sch Engn Polytech Sch Mesa AZ 85212 USA

ISBN: (纸本)9780791841037

This paper proposes an adaptive sampling algorithm for a pair of smart shoes for patients to use as a daily health monitoring device. The main hardware of the smart shoes features four pneumatic pressure sensors that measure ground contact forces (GCFs) and a global positioning system (GPS) to track the location of the user. The sampling rate of the pressure sensors and the GPS are changed based on the activity, either walking or sitting, detected from the user's GCFs. An outdoor test was conducted to validate the adaptive sampling algorithm. The result was a 95% reduction in data size compared to sampling with the highest settings from all components. Collected GPS information from a subject's morning activities was displayed onto a map to demonstrate how it could be used as contextual data for daily monitoring.

关键词： wearable sensors adaptive sampling algorithm gait contextual data patient monitoring device

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A Novel On-Machine Measurement Method Based on the Force Controlled Touch Probe 11th

A Novel On-Machine Measurement Method Based on the Force Con...

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11th International Conference on Intelligent Robotics and Applications (ICIRA)

作者： Li, Hao Zhao, Huan Ding, Han Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9783319975863;9783319975856

For free form surfaces such as blades, their measurement accuracy and efficiency depend on the planning strategy of discrete sampling points and the compensation precision of probe tip error. However, the force information is not considered in the traditional adaptive sampling and error compensation methods, which may affect the measurement efficiency and accuracy. In this paper, a novel force controlled touch probe for on-machine measurement system with a 5-DOF force/moment sensor is proposed. According to the projection of the force on cross section of blade, the proposed adaptive sampling algorithm can automatically select measuring points. Meanwhile, the direction of measuring force is used to estimate the compensation direction for probe tip error. The experiments are conducted on the on-machine measurement system, and the results demonstrate that the proposed methods are effective, which can provide important reference value for free-form surface measurement.

关键词： Force controlled touch probe On-machine measurement adaptive sampling algorithm Measurement error compensation

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Decentralized Control of adaptive sampling in Wireless Sensor Networks

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ACM TRANSACTIONS ON SENSOR NETWORKS 2009年第3期5卷 1–35页

作者： Kho, Johnsen Rogers, Alex Jennings, Nicholas R. Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England

The efficient allocation of the limited energy resources of a wireless sensor network in a way that maximizes the information value of the data collected is a significant research challenge. Within this context, this article concentrates on adaptive sampling as a means of focusing a sensor's energy consumption on obtaining the most important data. Specifically, we develop a principled information metric based upon Fisher information and Gaussian process regression that allows the information content of a sensor's observations to be expressed. We then use this metric to derive three novel decentralized control algorithms for information-based adaptive sampling which represent a trade-off in computational cost and optimality. These algorithms are evaluated in the context of a deployed sensor network in the domain of flood monitoring. The most computationally efficient of the three is shown to increase the value of information gathered by approximately 83%, 27%, and 8% per day compared to benchmarks that sample in a naive nonadaptive manner, in a uniform nonadaptive manner, and using a state-of-the-art adaptive sampling heuristic (USAC) correspondingly. Moreover, our algorithm collects information whose total value is approximately 75% of the optimal solution (which requires an exponential, and thus impractical, amount of time to compute).

关键词： algorithms Management Measurement adaptive sampling algorithm decentralized decision mechanism Gaussian process regression information metric

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